Google introduced the Agent2Agent (A2A) protocol, an open standard for communication between independent AI agents, enabling interoperability across different frameworks and organizations. The protoco…
A new guide details how to monitor LLM inference in production using Prometheus and Grafana, covering metrics like tokens/sec, queue duration, and KV cache pressure for servers such as vLLM, TGI, and …
LLM costs scale linearly with usage, and enterprises spending over $10,000 annually can optimize by implementing token budgets, choosing between API and local inference, and using fallback strategies.…
LLM guardrails control risk by constraining model behavior through input validation, output filtering, and fact-checking. Key strategies include prompt sanitization, length limits, content filtering, …
Multi-model system design involves orchestrating multiple AI models to handle complex tasks that a single model cannot. Five key architecture patterns—single model, sequential, parallel, hierarchical,…
Memory systems in AI assistants are evolving beyond the short-term versus long-term split, with OpenAI and LangGraph SDKs advocating for a three-tier stack of working memory, durable state, and retrie…
A production AI assistant requires a systems-level architecture beyond a single LLM invocation, splitting responsibilities into five layers: LLM, Memory, Tooling, Routing, and Observability. OpenAI an…
Microsoft's Work Trend Index and NIST's AI RMF argue that AI should augment rather than replace knowledge management, with trustworthy systems requiring explicit roles and oversight. Practical workflo…
Open-source AI agent frameworks OpenClaw and Hermes Agent dominate GitHub in 2026, with OpenClaw reaching 373,616 stars and Hermes Agent surpassing it in daily token processing on OpenRouter. OpenClaw…